A Machine Learning Approach to Continuous Vital Sign Data Analysis

This study is enrolling participants by invitation only.

Sponsor:

University of Colorado, Denver

ClinicalTrials.gov Identifier:

NCT01448161

First Posted: October 7, 2011

Last Update Posted: December 2, 2016

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government.
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Study hypothesis: Machine Learning algorithms and techniques previously developed for use in the robotics field can be applied to the field of medicine. These state-of-the-art, feature extraction and machine learning techniques can utilize patient vital sign data from bedside monitors to discover hidden relationships within the physiological waveforms and identify physiological trends or concerning conditions that are predictive of various clinical events. These algorithms could potentially provide preemptive alerts to clinicians of a developing patient problem, well before any human could detect a worrisome combination of events or trend in the data.

Specific aims:

Collect physiological waveform and numeric trend data from patient vital signs monitors in ICUs at the University of Colorado Hospital and Children's Hospital Colorado.

Apply Machine Learning techniques to these models to identify physiological waveform features and trend information, which are characteristic and predictive of common clinical conditions including but not limited to:

The Primary outcome utilized in this study will be the identification of the most relevant clinical features for detecting a chosen clinical event as determined by the Machine Learning feature-extraction techniques.

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Ages Eligible for Study:

31 Days to 89 Years (Child, Adult, Senior)

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

No

Sampling Method:

Non-Probability Sample

Study Population

Pediatric and Adult ICU patients

Criteria

Inclusion Criteria:

Age: 31 days - 89 years

Admitted to the surgical intensive care unit (SICU) at the University of Colorado Hospital or to the pediatric intensive care unit (PICU) or children's intensive care unit (CICU) at Children's Hospital Colorado or patients in the Childrens Hospital Colorado (CHC) emergency room with the following conditions

Hemodynamic instability

Febrile >38.5

Respiratory distress

Requiring mechanical ventilation

Requiring central access

Requiring vasoactive medications As well as the time that any of these patients might be in the operating rooms at Children's Hospital Colorado.

Exclusion Criteria:

Pregnant

Incarcerated

Limited access to or compromised monitoring sites for non-invasive finger and forehead sensors

Brain death (GCS 3 with fixed, dilated pupils)

Contacts and Locations

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01448161